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On October 21, 2026, Sarvam AI introduced a knowledge fabric architecture built for enterprise agents. The architecture consists of two layers, a knowledge tree and a domain ontology, compiling documents once into a structured layer that agents can navigate, query, and cite. According to its published results, on the Harvey LAB benchmark, answers using GLM 5.3 met 84.2% of expert grading criteria, higher than agentic RAG's 55.1%, with average task cost dropping from $0.64 to $0.28; on Sierra's tau-knowledge banking benchmark, it achieved accuracy comparable to the baseline using 64% fewer tokens. The knowledge tree is already used in the Sarvam cowork product, while the ontology is built domain by domain and enterprise client by enterprise client.
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Sarvam AI releases enterprise agent knowledge fabric architecture and benchmark resultsDevelopments
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Sarvam AI introduces knowledge fabric architecture for enterprise agents
Source:Sarvam AI Blog
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Oct 21
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Sarvam AI releases enterprise agent knowledge fabric architecture and benchmark results
Sarvam AI introduced a knowledge fabric built for enterprise agents, composed of two layers, a knowledge tree and a domain ontology, which compiles documents once into a structured layer that agents can navigate, query, and cite. On the Harvey LAB benchmark, answers using GLM 5.3 met 84.2% of expert grading criteria, higher than agentic RAG's 55.1%, with average task cost dropping from $0.64 to $0.28; on Sierra's tau-knowledge banking benchmark, it achieved accuracy comparable to the baseline with 64% fewer tokens. The knowledge tree is already used in the Sarvam cowork product, while the ontology is built domain by domain and enterprise customer by enterprise customer.